ΑΙhub.org
 

Machine learning enhances monitoring of threatened marbled murrelet


by
16 May 2024



share this:

Marbled murrelet. Photo credit: Brett Lovelace.

By Steve Lundeberg

Machine learning analysis of data gathered by acoustic recording devices is a promising new tool for monitoring the marbled murrelet and other secretive, hard-to-study species, research by Oregon State University and the U.S. Forest Service has shown.

The threatened marbled murrelet is an iconic Pacific Northwest seabird that’s closely related to puffins and murres, but unlike those birds, murrelets raise their young as far as 60 miles inland in mature and old-growth forests.

“There are very few species like it,” said co-author Matt Betts of the OSU College of Forestry. “And there’s no other bird that feeds in the ocean and travels such long distances to inland nest sites. This behavior is super unusual and it makes studying this bird really challenging.”

A research team led by Adam Duarte of the U.S. Forest Service’s Pacific Northwest Research Station used data from acoustic recorders, originally placed to assist in monitoring northern spotted owl populations, at thousands of locations in federally managed forests in the Oregon Coast Range and Washington’s Olympic Peninsula.

Researchers developed a machine learning algorithm known as a convolutional neural network to mine the recordings for murrelet calls.

Findings, published in Ecological Indicators, were tested against known murrelet population data and determined to be correct at a rate exceeding 90%, meaning the recorders and AI are able to provide an accurate look at how much murrelets are calling in a given area.

“Next, we’re testing whether murrelet sounds can actually predict reproduction and occupancy in the species, but that is still a few steps off,” Betts said.

The dove-sized marbled murrelet spends most of its time in coastal waters eating krill, other invertebrates and forage fish such as herring, anchovies, smelt and capelin. Murrelets can only produce one offspring per year, if the nest is successful, and their young require forage fish for proper growth and development.

The birds typically lay their single egg high in a tree on a horizontal limb at least 4 inches in diameter. Steller’s jays, crows and ravens are the main predators of murrelet nests.

Along the West Coast, marbled murrelets are found regularly from Santa Cruz, California, to the Aleutian Islands. The species is listed as threatened under the U.S. Endangered Species Act in Washington, Oregon and California.

“The greatest number of detections in our study typically occurred where late-successional forest dominates, and nearer to ocean habitats,” Duarte said.

Late-successional refers to mature and old-growth forests.

“Our results offer considerable promise for species distribution modeling and long-term population monitoring for rare species,” Duarte said. “Monitoring that’s far less labor intensive than nest searching via telemetry, ground-based nest searches or traditional audio/visual techniques.”

Matthew Weldy of the College of Forestry, Zachary Ruff of the OSU College of Agricultural Sciences and Jonathon Valente, a former Oregon State postdoctoral researcher now at the U.S. Geological Survey, joined Betts and Duarte in the study, along with Damon Lesmeister and Julianna Jenkins of the Forest Service.

Funding was provided by the Forest Service, the Bureau of Land Management and the National Park Service.

Read the work in full

Passive acoustic monitoring and convolutional neural networks facilitate high-resolution and broadscale monitoring of a threatened species, Adam Duarte, Matthew J. Weldy, Damon B. Lesmeister, Zachary J. Ruff, Julianna M.A. Jenkins, Jonathon J. Valente, Matthew G. Betts.




Oregon State University




            AIhub is supported by:



Related posts :



The Machine Ethics podcast: What excites you about AI? Vol.2

This is a bonus episode looking back over answers to our question: What excites you about AI?

Interview with Janice Anta Zebaze: using AI to address energy supply challenges

  07 Oct 2025
Find out more about research combining renewable energy systems, tribology, and artificial intelligence.

How does AI affect how we learn? A cognitive psychologist explains why you learn when the work is hard

  06 Oct 2025
Early research is only beginning to scratch the surface of how AI technology will truly affect learning and cognition in the long run.

Interview with Zahra Ghorrati: developing frameworks for human activity recognition using wearable sensors

  03 Oct 2025
Find out more about research developing scalable and adaptive deep learning frameworks.

Diffusion beats autoregressive in data-constrained settings

  03 Oct 2025
How can we trade off more compute for less data?

Forthcoming machine learning and AI seminars: October 2025 edition

  02 Oct 2025
A list of free-to-attend AI-related seminars that are scheduled to take place between 3 October and 30 November 2025.
monthly digest

AIhub monthly digest: September 2025 – conference reviewing, soccer ball detection, and memory traces

  30 Sep 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

Botanical time machines: AI is unlocking a treasure trove of data held in herbarium collections

  29 Sep 2025
New research describes the development and testing of a new AI-driven tool.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence